new structure of the future BigDL tutorial

README.md

@@ -2,6 +2,61 @@
 
 Step-by-step Deep Leaning Tutorials on Apache Spark using [BigDL](https://github.com/intel-analytics/BigDL/). The tutorials are inspired by [Apache Spark examples](http://spark.apache.org/examples.html), the [Theano Tutorials](https://github.com/Newmu/Theano-Tutorials) and the [Tensorflow tutorials](https://github.com/nlintz/TensorFlow-Tutorials).
 
+- Preface
+  - prologgue
+  - Organization of the tutorials
+  - advices and prerequisites for learners
+
+ - Introduction to Spark basics(topic 1-4)
+   - introduction to What Spark is, current usage and application(provided by useful links from Spark official site)
+   - environment setting and install instructions
+   - RDD
+   - DataFrame
+   - SparkSAL
+   - Structured Streaming
+
+ - Supervised Learning with BigDL(topic 6-8)
+   - install dependencies and set up the envrionment(imports)
+   - Introduction to Supervised Learning
+     - Linear Regression with BigDL(topic 6)
+       - About batch training
+       - Data Generation
+       - Hyperperameter setup
+       - model creation with Linear layer
+       - Loss function
+       - Optimizer
+       - Execute Training
+       - Prediction on training data
+       - Model evaluation on random test data
+
+     - Binary classification with logistic regression
+       - similar to structure in "Linear Regression with BigDL" but we bring the introduction to 
+         "BigDL's train_summary and validation summary API" and "how to use them to visualize the learing curve" here
+
+     - Multiclass classification with logistic regression(topic 8)
+       - introduction to MNIST dataset (topic 7)
+       - rest is same to the structure in "Linear Regression with BigDL"
+
+     - Overfitting and Regularization with BigDL
+       - What is overfitting with example
+       - Use regulariztion to solve overfitting
+       - regularization in BigDL
+
+
+ - Nerual Networks with BigDL
+   - Introduction to neural networks
+   - install dependencies and imports
+   - mechanics of weight and gradient update
+     - Forward and backward(topic 5)
+   - Feedforward Neural Network(topic 9)
+   - RNN(topic 11)
+   - Bi-RNN(topic 13)
+   - LSTM(topic 12)
+   - CNN(topic 10 will include "batch normalization" here)
+     - batch normalization
+   - Auto-encoder(topic 14)
+
+    *These neural network topics will have the same structure in "Binary classification with logistic regression"*
 ### Topics
 1. [RDD](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/RDD.ipynb) 
 2. [DataFrame](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/DataFrame.ipynb)
@@ -18,21 +73,4 @@ Step-by-step Deep Leaning Tutorials on Apache Spark using [BigDL](https://github
 13. [Bi-directional RNN](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/birnn.ipynb)
 14. [Auto-encoder](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/autoencoder.ipynb)
 
-### Environment
-+ Python 2.7
-+ JDK 8
-+ Apache Spark 2.2.0
-+ Jupyter Notebook 4.1
-+ BigDL 0.3.0
-+ [Setup env on Mac OS](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/SetupMac.md) / [Setup env on Linux](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/SetupLinux.md)
 
-### Start Jupyter Server
-* Run ```pip install BigDL==0.3.0```
-* Run ``` jupyter notebook --notebook-dir=./ --ip=* --no-browser```
-
-## Run Demo
-* Open a browser - Suggest Chrome or Firefox or Safari
-* Access notebook client at address http://localhost:8888, open the example ipynb files and execute.
-
-## Note
-* This notebook is for BigDL 0.3.0. Please refer branch-0.2 if you need to use BigDL 0.2.0.